Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

TL;DR

This study used a randomized controlled trial to assess how early-2025 AI tools impact experienced open-source developers' productivity, revealing that AI unexpectedly slowed down their task completion times.

Contribution

It provides the first experimental evidence that AI tools can increase developer task completion times, challenging optimistic predictions.

Findings

AI tools increased completion time by 19%.

Developers' post-study estimates underestimated the slowdown.

Robustness analysis suggests the slowdown is unlikely due to experimental artifacts.

Abstract

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.